In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
import numpy as np

beverage_df= pd.read_csv('data.csv')
print(beverage_df)

        ID  fixed acidity  volatile acidity  citric acid  residual sugar  \
0        1            7.4             0.700         0.00             1.9   
1        2            7.8             0.880         0.00             2.6   
2        3            7.8             0.760         0.04             2.3   
3        4           11.2             0.280         0.56             1.9   
4        5            7.4             0.700         0.00             1.9   
...    ...            ...               ...          ...             ...   
1596  1597            5.9             0.550         0.10             2.2   
1597  1598            6.3             0.510         0.13             2.3   
1598  1599            5.9             0.645         0.12             2.0   
1599  1600            6.0             0.310         0.47             3.6   
1600  1601            7.2             0.390         0.44             2.6   

      chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \
0         0.076                 11.0                  34.0  0.99780  3.51   
1         0.098                 25.0                  67.0  0.99680  3.20   
2         0.092                 15.0                  54.0  0.99700   NaN   
3         0.075                 17.0                  60.0  0.99800  3.16   
4         0.076                 11.0                  34.0  0.99780  3.51   
...         ...                  ...                   ...      ...   ...   
1596      0.062                 39.0                  51.0  0.99512  3.52   
1597      0.076                 29.0                  40.0  0.99574  3.42   
1598      0.075                 32.0                  44.0  0.99547  3.57   
1599      0.067                 18.0                  42.0  0.99549  3.39   
1600      0.066                 22.0                  48.0  0.99494  3.30   

      sulphates  alcohol  quality  
0          0.56      9.4      5.0  
1          0.68      9.8      5.0  
2          0.65      9.8      5.0  
3          0.58      9.8      6.0  
4          0.56      9.4      5.0  
...         ...      ...      ...  
1596       0.76     11.2      6.0  
1597       0.75     11.0      6.0  
1598       0.71     10.2      5.0  
1599       0.66     11.0      6.0  
1600       0.84     11.5      6.0  

[1601 rows x 13 columns]
In [2]:
beverage_df.duplicated()
Out[2]:
0       False
1       False
2       False
3       False
4       False
        ...  
1596    False
1597    False
1598    False
1599    False
1600    False
Length: 1601, dtype: bool
In [3]:
beverage_df.duplicated().sum()
Out[3]:
0
In [4]:
beverage_df1 = beverage_df.drop_duplicates()
beverage_df1.shape
Out[4]:
(1601, 13)
In [5]:
beverage_df.isnull()
Out[5]:
ID fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 False False False False False False False False False False False False False
1 False False False False False False False False False False False False False
2 False False False False False False False False False True False False False
3 False False False False False False False False False False False False False
4 False False False False False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ...
1596 False False False False False False False False False False False False False
1597 False False False False False False False False False False False False False
1598 False False False False False False False False False False False False False
1599 False False False False False False False False False False False False False
1600 False False False False False False False False False False False False False

1601 rows × 13 columns

In [6]:
beverage_df.isnull().sum()
Out[6]:
ID                      0
fixed acidity           1
volatile acidity        0
citric acid             1
residual sugar          0
chlorides               3
free sulfur dioxide     1
total sulfur dioxide    1
density                 1
pH                      1
sulphates               0
alcohol                 0
quality                 1
dtype: int64
In [7]:
updatedbeverage_df = beverage_df.dropna()
In [8]:
updatedbeverage_df.isnull().sum()
Out[8]:
ID                      0
fixed acidity           0
volatile acidity        0
citric acid             0
residual sugar          0
chlorides               0
free sulfur dioxide     0
total sulfur dioxide    0
density                 0
pH                      0
sulphates               0
alcohol                 0
quality                 0
dtype: int64

METHOD USED TO REMOVE NULL VALUES AND REASON¶

METHOD:REMOVE ROWS WITH NULL VALUES¶

REASON: After checking the number of null values,I realized there are only a few null values and knowing that deleting these values will not cause adverse effects on my entire result,I decided to remove them from my DataFrame.¶

In [9]:
beverage_df=updatedbeverage_df.dropna()
In [10]:
beverage_df.describe()
Out[10]:
ID fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
count 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000 1591.000000
mean 801.060968 8.320616 0.527681 0.271477 2.540761 0.087500 15.861722 46.498429 0.996747 3.311358 0.658611 10.426053 5.637964
std 460.740942 1.740134 0.179045 0.194807 1.413076 0.047175 10.429385 32.998659 0.001889 0.154548 0.169786 1.066706 0.807772
min 1.000000 4.600000 0.120000 0.000000 0.900000 0.012000 1.000000 6.000000 0.990070 2.740000 0.330000 8.400000 3.000000
25% 402.500000 7.100000 0.390000 0.090000 1.900000 0.070000 7.000000 22.000000 0.995600 3.210000 0.550000 9.500000 5.000000
50% 801.000000 7.900000 0.520000 0.260000 2.200000 0.079000 14.000000 38.000000 0.996750 3.310000 0.620000 10.200000 6.000000
75% 1199.500000 9.200000 0.640000 0.420000 2.600000 0.090000 21.000000 62.000000 0.997845 3.400000 0.730000 11.100000 6.000000
max 1601.000000 15.900000 1.580000 1.000000 15.500000 0.611000 72.000000 289.000000 1.003690 4.010000 2.000000 14.900000 8.000000
In [11]:
beverage_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1591 entries, 0 to 1600
Data columns (total 13 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   ID                    1591 non-null   int64  
 1   fixed acidity         1591 non-null   float64
 2   volatile acidity      1591 non-null   float64
 3   citric acid           1591 non-null   float64
 4   residual sugar        1591 non-null   float64
 5   chlorides             1591 non-null   float64
 6   free sulfur dioxide   1591 non-null   float64
 7   total sulfur dioxide  1591 non-null   float64
 8   density               1591 non-null   float64
 9   pH                    1591 non-null   float64
 10  sulphates             1591 non-null   float64
 11  alcohol               1591 non-null   float64
 12  quality               1591 non-null   float64
dtypes: float64(12), int64(1)
memory usage: 174.0 KB
In [12]:
beverage_df = beverage_df.rename(columns={'fixed acidity':'fixed_acidity',
                                  'volatile acidity':'volatile_acidity',
                                  'citric acid':'citric_acid',
                                  'residual sugar':'residual_sugar',
                                  'free sulfur dioxide':'free_sulfur_dioxide',
                                  'total sulfur dioxide':'total_sulfur_dioxide'})
beverage_df.columns
Out[12]:
Index(['ID', 'fixed_acidity', 'volatile_acidity', 'citric_acid',
       'residual_sugar', 'chlorides', 'free_sulfur_dioxide',
       'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol',
       'quality'],
      dtype='object')
In [13]:
X = beverage_df['alcohol']
Y = beverage_df['quality']
plt.scatter(X, Y)
plt.title('Scatter Plot of Alcohol Content vs Quality of Beverage')
plt.xlabel('Alcohol Content')
plt.ylabel('Quality')
plt.show()

STATISTICAL INFERENCE OF INDIVIDUAL DATA SETS¶

METHOD USED:

PANDAS AND YDATA_PROFILING WHICH ARE PRE-CREATED LIBRARIES WERE INSTALLED AND IMPORTED INTO JUPYTER NOTEBOOK.

THESE PROFILING LIBRARIES GIVE DETAILED STATISTICAL INFERENCES OF OUR DATA BY PROVIDING HISTOGRAMS,HEATMAPS AND CORRELATION DATA.

In [14]:
from pandas_profiling import ProfileReport
profile = ProfileReport(beverage_df, title="Data Report")

C:\Users\Damian Yawuliga\AppData\Local\Temp\ipykernel_4632\1269014081.py:1: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead.
  from pandas_profiling import ProfileReport
In [15]:
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[15]:
























In [16]:



     


sn.pairplot(beverage_df);



     




















    
    































In [17]:



     


plt.figure(figsize = [15,10])
sn.heatmap(beverage_df.corr(), annot = True)



     




















    
    
Out[17]:








<AxesSubplot: >








    
    



































As one of the key steps in data analysis , we are expected to  Ask & answer questions about the data.
Below are some questions I asked myself and answered them using diagrams or plots.























Question 1) what is the quality of alcohol in most red wine beverages produced.



















In [21]:



     


sn.countplot(x= 'quality', data =beverage_df)



     




















    
    
Out[21]:








<AxesSubplot: xlabel='quality', ylabel='count'>








    
    































In [24]:



     


beverage_df.groupby(['quality']).sum().plot(kind='pie', y='alcohol', autopct='%1.0f%%')



     




















    
    
Out[24]:








<AxesSubplot: ylabel='alcohol'>








    
    



































It can be seen from the histogram and pie chart that majority of the red wine beverage have alcohol quality of 5 and 6.¶






















Question 2) If  red wine  of quality 6 and above is considered good, what is the percentage of good quality red wine produced.



















In [20]:



     


beverage_df[beverage_df['quality'] > 5.0].count()



     




















    
    
Out[20]:








ID                      854
fixed_acidity           854
volatile_acidity        854
citric_acid             854
residual_sugar          854
chlorides               854
free_sulfur_dioxide     854
total_sulfur_dioxide    854
density                 854
pH                      854
sulphates               854
alcohol                 854
quality                 854
dtype: int64




























*From this above data,dividing 854 by total number of rows which is 1591 multiplied by 100% gives us the percentage of good quality red wine produced.*



















In [25]:



     


beverage_df[beverage_df.quality>5].count()/beverage_df.count()



     




















    
    
Out[25]:








ID                      0.536769
fixed_acidity           0.536769
volatile_acidity        0.536769
citric_acid             0.536769
residual_sugar          0.536769
chlorides               0.536769
free_sulfur_dioxide     0.536769
total_sulfur_dioxide    0.536769
density                 0.536769
pH                      0.536769
sulphates               0.536769
alcohol                 0.536769
quality                 0.536769
dtype: float64




























FROM THE ABOVE DATA IT CAN BE SEEN THAT  53.68% OF RED WINE PRODUCED ARE OF GOOD QUALITY,WHICH IS THE SAME FOR THE FIRST METHOD USED.























Question 3) if RED WINE of quality 5 and below is considered bad, what is the percentage of bad quality red wine produced.



















In [28]:



     


beverage_df[beverage_df.quality<6].count()/beverage_df.count()



     




















    
    
Out[28]:








ID                      0.463231
fixed_acidity           0.463231
volatile_acidity        0.463231
citric_acid             0.463231
residual_sugar          0.463231
chlorides               0.463231
free_sulfur_dioxide     0.463231
total_sulfur_dioxide    0.463231
density                 0.463231
pH                      0.463231
sulphates               0.463231
alcohol                 0.463231
quality                 0.463231
dtype: float64




























FROM THE ABOVE DATA IT CAN BE SEEN THAT  46.32% OF RED WINE PRODUCED ARE OF BAD QUALITY























Some Inferences and Conclusion¶
After analysing the dataset through plots and diagrams it can be seen that :


Majority of the RED WINE produced have alcohol quantity of 5 and 6


53.68% OF RED WINE PRODUCED ARE OF GOOD QUALITY


46.32% OF RED WINE PRODUCED ARE OF BAD QUALITY